Multi-energy blended CBCT spectral imaging and scatter-decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS).

BACKGROUND: Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity. PURPOSE: The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections. METHODS: To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method. RESULTS: Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values ( E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, the E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively. CONCLUSIONS: We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors.

BACKGROUND: Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store. PURPOSE: This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors. METHODS: We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramér-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area density ρ A , m ${\rho }_{A,m}$ (0-40 g/cm2 water, 0-2.16 g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively. RESULTS: The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively. CONCLUSIONS: We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.

Single-shot quantitative x-ray imaging using a primary modulator and dual-layer detector.

BACKGROUND: Conventional x-ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. Dual-energy (DE) imaging, with its capability to quantify area density of specific materials, is well-suited to address such limitations, but only if the dual-energy projections are acquired with perfect spatial and temporal alignment and corrected for scatter. PURPOSE: In this work, we propose single-shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual-layer (DL) detector, which enables motion-free DE imaging with scatter correction in a single exposure. METHODS: The key components of our SSQI setup include a PM and DL detector, where the former enables scatter correction for the latter while the latter enables beam hardening correction for the former. The SSQI algorithm allows simultaneous recovery of two material-specific images and two scatter images using four sub-measurements from the PM encoding. The concept was first demonstrated using simulation of chest x-ray imaging for a COVID patient. For validation, we set up SSQI geometry on our tabletop system and imaged acrylic and copper slabs with known thicknesses (acrylic: 0-22.5 cm; copper: 0-0.9 mm), estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast. RESULTS: Our simulation study demonstrated that SSQI led to accurate scatter correction and MD, particularly for smaller focal blur and finer PM pitch. In the validation study, we found that the root mean squared error (RMSE) of SSQI estimation was 0.13 cm for acrylic and 0.04 mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them quantitatively, reducing RMSE in material-specific images by 38%-92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background. CONCLUSIONS: We demonstrated the potential of SSQI for robust quantitative x-ray imaging. The integration of SSQI is straightforward with the addition of a PM and upgrade to a DL detector, which may enable its widespread adoption, including in techniques such as radiography and dynamic imaging (i.e., real-time image guidance and cone-beam CT).

A Multi-Functional CMOS Biosensor Array With On-Chip DEP-Assisted Sensing for Rapid Low-Concentration Analyte Detection and Close-Loop Particle Manipulation With No External Electrodes.

This article presents a fully-integrated dielectrophoresis (DEP)-assisted multi-functional CMOS biosensor array chip with 4096 working electrodes (WEs), 12288 photodiodes (PDs), reference electrodes (REs), and counter electrodes (CEs), while each WE and photodiode can be reconfigured to support on-chip DEP actuation, electrochemical potentiostat, optical shadow imaging, and complex impedance sensing. The proposed CMOS biosensor is an example of an actuation-assisted label-free biosensor for the rapid sensing of low-concentration analytes. The DEP actuator of the proposed CMOS biosensor does not require any external electrode. Instead, on-chip WE pairs can be re-used for DEP actuation to simplify the sensor array design. The CMOS biosensor is implemented in a standard 130-nm BiCMOS process. Theoretical analyses and finite element method (FEM) simulations of the on-chip DEP operations are conducted as proof of concept. Biological assay measurements (DEP actuation/electrochemical potentiostat/impedance sensing) with E.coli bacteria and microbeads (optical shadow imaging) demonstrate rapid detection of low-concentration analytes and simultaneous manipulation and detection of large particles. The on-chip DEP operations draw the analytes closer to the sensor electrode surface, which overcomes the diffusion limit and accelerates low-concentration analyte sensing. Moreover, the DEP-based movement of large particles can be readily detected by on-chip photodiode arrays to achieve close-loop manipulation and sensing of particles and droplets. These show the unique advantages of the DEP-assisted multi-functional biosensor.

X-Ray Imaging in the Simulated Microgravity Environment of Parabolic Flight.

INTRODUCTION: The advancement of human spaceflight has made urgent the need to develop medical imaging technology to ensure a high level of in-flight care. To date, only ultrasound has been used in spaceflight. Radiography has multiple advantages over ultrasound, including lower operator dependence, more rapid acquisition, typically higher spatial resolution, and characterization of tissue with acoustic impedance precluding ultrasound. This proof-of-concept work demonstrates for the first time the feasibility of performing human radiographs in microgravity.METHODS: Radiographs of a phantom and human subject's hand, knee, chest, cervical spine, and pelvis were obtained aboard a parabolic flight in microgravity and simulated lunar gravity with various subject and operator positions. Control radiographs were acquired with the same system on the ground. These radiographs were performed with a Food and Drug Administration-approved ultra-portable, wireless, battery-powered, digital x-ray system.RESULTS: The radiographs of the phantom acquired in reduced gravity were qualitatively and quantitatively compared to the ground controls and found to exhibit similar diagnostic adequacy. There was no statistically significant difference in contrast resolution or spatial resolution with a spatial resolution across all imaging environments up to the Nyquist frequency of 3.6 line-pairs/mm and an average contrast-to-noise ratio of 2.44.DISCUSSION: As mass, power, and volume limitations lessen over the coming decades and the miniaturization of imaging equipment continues, in-flight implementation of nonsonographic modalities will become practical. Given the demonstrated ease of use and satisfactory image quality, portable radiography is ready to be the new frontier of space medical imaging.Lerner D, Pohlen M, Wang A, Walter J, Cairnie M, Gifford S. X-ray imaging in the simulated microgravity environment of parabolic flight. Aerosp Med Hum Perform. 2023; 94(10):786-791.

Synthetic dual-energy CT reconstruction from single-energy CT Using artificial intelligence.

PURPOSE: To develop and assess the utility of synthetic dual-energy CT (sDECT) images generated from single-energy CT (SECT) using two state-of-the-art generative adversarial network (GAN) architectures for artificial intelligence-based image translation. METHODS: In this retrospective study, 734 patients (389F; 62.8 years ± 14.9) who underwent enhanced DECT of the chest, abdomen, and pelvis between January 2018 and June 2019 were included. Using 70-keV as the input images (n = 141,009) and 50-keV, iodine, and virtual unenhanced (VUE) images as outputs, separate models were trained using Pix2PixHD and CycleGAN. Model performance on the test set (n = 17,839) was evaluated using mean squared error, structural similarity index, and peak signal-to-noise ratio. To objectively test the utility of these models, synthetic iodine material density and 50-keV images were generated from SECT images of 16 patients with gastrointestinal bleeding performed at another institution. The conspicuity of gastrointestinal bleeding using sDECT was compared to portal venous phase SECT. Synthetic VUE images were generated from 37 patients who underwent a CT urogram at another institution and model performance was compared to true unenhanced images. RESULTS: sDECT from both Pix2PixHD and CycleGAN were qualitatively indistinguishable from true DECT by a board-certified radiologist (avg accuracy 64.5%). Pix2PixHD had better quantitative performance compared to CycleGAN (e.g., structural similarity index for iodine: 87% vs. 46%, p-value < 0.001). sDECT using Pix2PixHD showed increased bleeding conspicuity for gastrointestinal bleeding and better removal of iodine on synthetic VUE compared to CycleGAN. CONCLUSIONS: sDECT from SECT using Pix2PixHD may afford some of the advantages of DECT.

Protocol to establish a genetically engineered mouse model of IDH1-mutant astrocytoma.

Lower-grade gliomas exhibit a high prevalence of isocitrate dehydrogenase 1 (IDH1) mutations, but faithful models for studying these tumors are lacking. Here, we present a protocol to establish a genetically engineered mouse (GEM) model of grade 3 astrocytoma driven by the Idh1R132H oncogene. We describe steps for breeding compound transgenic mice and intracranially delivering adeno-associated virus particles, followed by post-surgical surveillance via magnetic resonance imaging. This protocol enables the generation and use of a GEM to study lower-grade IDH-mutant gliomas. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022).1.

High resolution imaging with focused kV x-rays for small animal radio-neuromodulation.

BACKGROUND: High precision radiotherapy with small irradiator size has potential in many treatment applications involving small shallow targets, with small animal radio-neuromodulation as an intriguing example. A focused kV technique based on novel usage of polycapillary x-ray lenses can focus x-ray beams to <0.2 mm in diameter, which is ideal for such uses. PURPOSE: Such an application also requires high resolution CT images for treatment planning and setup. In this work, we demonstrate the feasibility of using a virtual focal spot generated with an x-ray lens to perform high-resolution CBCT acquisition. METHOD: The experiment with x-ray lens was set up on an x-ray tabletop system to generate a virtual focal spot. The flood field images with and without the x-ray lens were first compared. A pinhole image was acquired for the virtual focal spot and compared with the one acquired with the conventional focal spot without the lens. The planar imaging resolution with and without the lens were evaluated using a line pair resolution phantom. The spatial resolution of the two settings were estimated by reconstructing a 0.15-mm wire phantom and comparing its full width half maximum (FWHM). A CBCT scan of a rodent head was also acquired to further demonstrate the improved resolution using the x-ray lens. RESULT: The proposed imaging setup with x-ray lens had a limited exposure area of 5 cm by 5 cm on the detector, which was suitable for guiding radio-neuromodulation to a small target in rodent brain. Compared to conventional imaging acquisition with a measured x-ray focal spot of 0.395 mm FWHM, the virtual focal spot size was measured at 0.175 mm. The reduction in focal spot size with lens leads to an almost doubled planar imaging resolution and a 26% enhancement in 3D spatial resolution. A realistic CBCT acquisition of a rodent head mimicked the imaging acquisition step for radio-neuromodulation and further showed the improved visualization for fine structures. CONCLUSION: This work demonstrated that the focused kV x-ray technique was capable of generating small focal spot size of <0.2 mm, which substantially improved x-ray imaging resolution for small animal imaging.

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

Single-Shot Quantitative X-ray Imaging Using a Primary Modulator and Dual-Layer Detector: Simulation and Phantom Studies.

Conventional x-ray imaging provides little quantitative information due to scatter, beam hardening, and overlaying tissues. A single-shot quantitative x-ray imaging (SSQI) method was previously developed to quantify material-specific densities in x-ray imaging by combining the use of a primary modulator (PM) and dual-layer (DL) detector. The feasibility of this concept was demonstrated with simulations using an iterative patch-based method. In this work, we propose a new algorithm pipeline for SSQI that enables accurate quantification and high computational efficiency. The DL images contain four measurements that are obtained behind the unattenuated and partially attenuated regions of the PM of each layer. Using the low-frequency property of scatter and a pre-calibrated material decomposition (MD), four unknowns (i.e., two scatter images and two material-specific images) are jointly recovered by directly solving four equations given by the four measurements. We tested this algorithm in simulations and further demonstrated its efficacy on chest phantom experiments. Through simulation, we show that the new method for MD is robust against scatter. Its performance improves with smaller PM pitch size and smaller focal spot blur. The RMSE in material-specific images compared to ground truth reduces by 52%-84% versus without scatter correction. For our experimental study, we successfully separated soft tissue and bone. The computational time for processing each view was ~8 s without optimization. The reported results further strengthen the potential of SSQI for widespread adoption, leading to quantitative imaging not only for x-ray imaging but also for real-time image guidance or cone-beam CT.

A Multimodal and Multifunctional CMOS Cellular Interfacing Array for Digital Physiology and Pathology Featuring an Ultra Dense Pixel Array and Reconfigurable Sampling Rate.

The article presents a fully integrated multimodal and multifunctional CMOS biosensing/actuating array chip and system for multi-dimensional cellular/tissue characterization. The CMOS chip supports up to 1,568 simultaneous parallel readout channels across 21,952 individually addressable multimodal pixels with 13 µm × 13 µm 2-D pixel pitch along with 1,568 Pt reference electrodes. These features allow the CMOS array chip to perform multimodal physiological measurements on living cell/tissue samples with both high throughput and single-cell resolution. Each pixel supports three sensing and one actuating modalities, each reconfigurable for different functionalities, in the form of full array (FA) or fast scan (FS) voltage recording schemes, bright/dim optical detection, 2-/4-point impedance sensing (ZS), and biphasic current stimulation (BCS) with adjustable stimulation area for single-cell or tissue-level stimulation. Each multi-modal pixel contains an 8.84 µm × 11 µm Pt electrode, 4.16 µm × 7.2 µm photodiode (PD), and in-pixel circuits for PD measurements and pixel selection. The chip is fabricated in a standard 130nm BiCMOS process as a proof of concept. The on-chip electrodes are constructed by unique design and in-house post-CMOS fabrication processes, including a critical Al shorting of all pixels during fabrication and Al etching after fabrication that ensures a high-yield planar electrode array on CMOS with high biocompatibility and long-term measurement reliability. For demonstration, extensive biological testing is performed with human and mouse progenitor cells, in which multidimensional biophysiological data are acquired for comprehensive cellular characterization.

Contrast solution properties and scan parameters influence the apparent diffusivity of computed tomography contrast agents in articular cartilage.

The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that blurs tissue boundaries in clinical arthrograms, contrast agent (CA) diffusivity can be derived from computed tomography arthrography (CTa) scans. We combined experimental and computational approaches to study protocol variations that may alter the CTa-derived apparent diffusivity. In experimental studies on bovine cartilage explants, we examined how CA dilution and transport direction (absorption versus desorption) influence the apparent diffusivity of untreated and enzymatically digested cartilage. Using multiphysics simulations, we examined mechanisms underlying experimental observations and the effects of image resolution, scan interval and early scan termination. The apparent diffusivity during absorption decreased with increasing CA concentration by an amount similar to the increase induced by tissue digestion. Models indicated that osmotically-induced fluid efflux strongly contributed to the concentration effect. Simulated changes to spatial resolution, scan spacing and total scan time all influenced the apparent diffusivity, indicating the importance of consistent protocols. With careful control of imaging protocols and interpretations guided by transport models, CTa-derived diffusivity offers promise as a biomarker for early degenerative changes.

De novo pyrimidine synthesis is a targetable vulnerability in IDH mutant glioma.

Mutations affecting isocitrate dehydrogenase (IDH) enzymes are prevalent in glioma, leukemia, and other cancers. Although mutant IDH inhibitors are effective against leukemia, they seem to be less active in aggressive glioma, underscoring the need for alternative treatment strategies. Through a chemical synthetic lethality screen, we discovered that IDH1-mutant glioma cells are hypersensitive to drugs targeting enzymes in the de novo pyrimidine nucleotide synthesis pathway, including dihydroorotate dehydrogenase (DHODH). We developed a genetically engineered mouse model of mutant IDH1-driven astrocytoma and used it and multiple patient-derived models to show that the brain-penetrant DHODH inhibitor BAY 2402234 displays monotherapy efficacy against IDH-mutant gliomas. Mechanistically, this reflects an obligate dependence of glioma cells on the de novo pyrimidine synthesis pathway and mutant IDH's ability to sensitize to DNA damage upon nucleotide pool imbalance. Our work outlines a tumor-selective, biomarker-guided therapeutic strategy that is poised for clinical translation.

Science and practice of imaging physics through 50 years of SPIE Medical Imaging conferences.

Purpose: For 50 years now, SPIE Medical Imaging (MI) conferences have been the premier forum for disseminating and sharing new ideas, technologies, and concepts on the physics of MI. Approach: Our overarching objective is to demonstrate and highlight the major trajectories of imaging physics and how they are informed by the community and science present and presented at SPIE MI conferences from its inception to now. Results: These contributions range from the development of image science, image quality metrology, and image reconstruction to digital x-ray detectors that have revolutionized MI modalities including radiography, mammography, fluoroscopy, tomosynthesis, and computed tomography (CT). Recent advances in detector technology such as photon-counting detectors continue to enable new capabilities in MI. Conclusion: As we celebrate the past 50 years, we are also excited about what the next 50 years of SPIE MI will bring to the physics of MI.

Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.

PURPOSE: This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation. METHODS: A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-group-specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ dose estimation. RESULTS: Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%. CONCLUSIONS: Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT dose estimation.

Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.

PURPOSE: Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. ACQUISITION AND VALIDATION METHODS: The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. DATA FORMAT AND USAGE NOTES: The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. POTENTIAL APPLICATIONS: This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.

An Updated Checklist of Sea Slugs (Gastropoda, Heterobranchia) from Hong Kong Supported by Citizen Science.

Research on heterobranch sea slug diversity in Hong Kong was flourishing in the 1980-90s, with the first checklist and guidebook published and many new species and records documented later. This golden era of sea slug research ended abruptly, and no review nor any extensive collection has been done in the past 20+ years. In view of the extensive revision in heterobranch systematics in recent years, the current checklist can be considered out of date in both nomenclature and composition. Therefore, we are dedicated to providing a synthesis of the current state of knowledge on Hong Kong sea slug diversity by reviewing previous records and engagement of citizen science. An integration of previously published and unpublished records revealed the presence of 186 species, of which 107 species were documented by our citizen science programme with a further report of 71 new records. This yields a total of 257 species from 126 genera, 53 families and ten higher taxa, of which seven species have not been reported elsewhere from Hong Kong. While the present study has markedly illuminated our understanding of sea slug diversity in Hong Kong, the true biodiversity may be underestimated given the high proportion of novel records, especially when many previous records were not herein observed. Hong Kong likely harbours a greater sea slug diversity which awaits discovery from more rigorous sampling and a more inclusive citizen science programme.

Upstream Machine Learning in Radiology.

Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.

Validation of a deterministic linear Boltzmann transport equation solver for rapid CT dose computation using physical dose measurements in pediatric phantoms.

PURPOSE: The risk of inducing cancer to patients undergoing CT examinations has motivated efforts for CT dose estimation, monitoring, and reduction, especially among pediatric population. The method investigated in this study is Acuros CTD (Varian Medical Systems, Palo Alto, CA), a deterministic linear Boltzmann transport equation (LBTE) solver aimed at generating rapid and reliable dose maps of CT exams. By applying organ contours, organ doses can also be obtained, thus patient-specific organ dose estimates can be provided. This study experimentally validated Acuros against measurements performed on a clinical CT system using a range of physical pediatric anthropomorphic phantoms and acquisition protocols. METHODS: The study consisted of (1) the acquisition of dose measurements on a clinical CT scanner through thermoluminescent dosimeters (TLDs), and (2) the modeling in the Acuros platform of the measurement set up, which includes the modeling of the CT scanner and of the anthropomorphic phantoms. For the measurements, 1-year-old, 5-year-old, and 10-year-old anthropomorphic phantoms of the CIRS ATOM family were used. TLDs were placed in selected organ locations such as stomach, liver, lungs, and heart. The pediatric phantoms were scanned helically with the GE Discovery 750 HD clinical scanner for several examination protocols. For the simulations in Acuros, scanner-specific input, such as bowtie filters, overrange collimation, and tube current modulation schemes, were modeled. These scanner complexities were implemented by defining discretized X-ray beams whose spectral distribution, defined in Acuros by only six energy bins, varied across fan angle, cone angle, and slice position. The images generated during the CT acquisitions were used to create the geometrical models, by applying thresholding algorithms and assigning materials to the HU values. The TLDs were contoured in the phantom models as sensitive cylindrical volumes at the locations selected for dosimeters placement, to provide dose estimates, in terms of dose per unit photon. To compare measured doses with dose estimates, a calibration factor was derived from the CTDIvol displayed by the scanner, to account for the number of photons emitted by the X-ray tube during the procedure. RESULTS: The differences of the measured and estimated doses, in terms of absolute % errors, were within 13% for 153 TLD locations, with an error of 17% at the stomach for one study with the 10-year-old phantom. Root-mean-squared-errors (RMSE) across all TLD locations for all configurations were in the range of 3%-8%, with Acuros providing dose estimates in a time range of a few seconds up to 2 min. CONCLUSIONS: An overall good agreement between measurements and simulations was achieved, with average RMSE of 6% across all cases. The results demonstrate that Acuros can model a specific clinical scanner despite the required discretization in spatial and energy domains. The proposed deterministic tool has the potential to be part of a near real-time individualized dosimetry monitoring system for CT applications, providing patient-specific organ dose estimates.

Characterization of x-ray focal spots using a rotating edge.

Purpose: The focal spot size and shape of an x-ray system are critical factors to the spatial resolution. Conventional approaches to characterizing the focal spot use specialized tools that usually require careful calibration. We propose an alternative to characterize the x-ray source's focal spot, simply using a rotating edge and flat-panel detector. Methods: An edge is moved to the beam axis, and an edge spread function (ESF) is obtained at a specific angle. Taking the derivative of the ESF provides the line spread function, which is the Radon transform of the focal spot in the direction parallel to the edge. By rotating the edge about the beam axis for 360 deg, we obtain a complete Radon transform, which is used for reconstructing the focal spot. We conducted a study on a clinical C-arm system with three focal spot sizes (0.3, 0.6, and 1.0 mm nominal size), then compared the focal spot imaged using the proposed method against the conventional pinhole approach. The full width at half maximum (FWHM) of the focal spots along the width and height of the focal spot were used for quantitative comparisons. Results: Using the pinhole method as ground truth, the proposed method accurately characterized the focal spot shapes and sizes. Quantitatively, the FWHM widths were 0.37, 0.65, and 1.14 mm for the pinhole method and 0.33, 0.60, and 1.15 mm for the proposed method for the 0.3, 0.6, and 1.0 mm nominal focal spots, respectively. Similar levels of agreement were found for the FWHM heights. Conclusions: The method uses a rotating edge to characterize the focal spot and could be automated in the future using a system's built-in collimator. The method could be included as part of quality assurance tests of image quality and tube health.